Geometric transformation-based data augmentation on defect classification of segmented images of semiconductor materials using a ResNet50 convolutional neural network

Rosa, Francisco López de la; Gómez-Sirvent, José L.; Sánchez-Reolid, Roberto; Morales, Rafael; Fernández-Caballero, Antonio

doi:10.1016/j.eswa.2022.117731

Cited by 45 publications

(18 citation statements)

References 25 publications

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“…For data augmentation, various types of techniques have been followed by the researcher over the last few decades. It is generally classified as an operation-based manipulation approach, a synthetic data generator approach, and some hybrid techniques are also proposed by different researchers for data augmentation ( Raj et al., 2022 ; Rosa et al., 2022 ).…”

Section: Related Workmentioning

confidence: 99%

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Developing precision agriculture using data augmentation framework for automatic identification of castor insect pests

Nitin

Gupta²,

Yadav³

et al. 2023

Front. Plant Sci.

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Castor (Ricinus communis L.) is an important nonedible industrial crop that produces oil, which is used in the production of medicines, lubricants, and other products. However, the quality and quantity of castor oil are critical factors that can be degraded by various insect pest attacks. The traditional method of identifying the correct category of pests required a significant amount of time and expertise. To solve this issue, automatic insect pest detection methods combined with precision agriculture can help farmers in providing adequate support for sustainable agriculture development. For accurate predictions, the recognition system requires a sufficient amount of data from a real-world situation, which is not always available. In this regard, data augmentation is a popular technique used for data enrichment. The research conducted in this investigation established an insect pest dataset of common castor pests. This paper proposes a hybrid manipulation-based approach for data augmentation to solve the issue of the lack of a suitable dataset for effective vision-based model training. The deep convolutional neural networks VGG16, VGG19, and ResNet50 are then adopted to analyze the effects of the proposed augmentation method. The prediction results show that the proposed method addresses the challenges associated with adequate dataset size and significantly improves overall performance when compared to previous methods.

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Section: Related Workmentioning

confidence: 99%

“…The timely collection of a sufficient quantity of datasets is essential for the proper implementation of the machine vision model ( Rosa et al., 2022 ). In contrast, Dieleman et al.…”

Section: Introductionmentioning

confidence: 99%

Developing precision agriculture using data augmentation framework for automatic identification of castor insect pests

Nitin

Gupta²,

Yadav³

et al. 2023

Front. Plant Sci.

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show abstract

“…However, the challenge of this study was how to find the optimal augmentation strength throughout training. De la Rosa et al 41 studied the effect of data augmentation on the performance of a ResNet-50 model in defect classification problems. Especially, in cases where the volumes of training images and balanced classes are small.…”

Section: Literature Reviewmentioning

confidence: 99%

A conditional GAN-based approach for enhancing transfer learning performance in few-shot HCR tasks

Elaraby

Barakat

Rezk

2022

Sci Rep

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Supervised learning with the restriction of a few existing training samples is called Few-Shot Learning. FSL is a subarea that puts deep learning performance in a gap, as building robust deep networks requires big training data. Using transfer learning in FSL tasks is an acceptable way to avoid the challenge of building new deep models from scratch. Transfer learning methodology considers borrowing the architecture and parameters of a previously trained model on a large-scale dataset and fine-tuning it for low-data target tasks. But practically, fine-tuning pretrained models in target FSL tasks suffers from overfitting. The few existing samples are not enough to correctly adjust the pretrained model’s parameters to provide the best fit for the target task. In this study, we consider mitigating the overfitting problem when applying transfer learning in few-shot Handwritten Character Recognition (HCR) tasks. A data augmentation approach based on Conditional Generative Adversarial Networks is introduced. CGAN is a generative model that can create artificial instances that appear more real and indistinguishable from the original samples. CGAN helps generate extra samples that hold the possible variations of human handwriting instead of applying traditional image transformations. These transformations are low-level, data-independent operations, and only produce augmented samples with limited diversity. The introduced approach was evaluated in fine-tuning the three pretrained models: AlexNet, VGG-16, and GoogleNet. The results show that the samples generated by CGAN can enhance transfer learning performance in few-shot HCR tasks. This is by achieving model fine-tuning with fewer epochs and by increasing the model’s $$F1-score$$ F 1 - s c o r e and decreasing the Generalization Error $$(E_{test})$$ ( E test ) .

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“…is can be done through geometric transformations, the use of kernels, etc. e latter leverages some DNN architectures, such as Generative Adversarial Networks (GANs), to learn the features of the original image to generate synthetic ones based on learned features [114].…”

Section: Preprocessing Images For Deep Learningmentioning

confidence: 99%

A Review on Thermal Imaging-Based Breast Cancer Detection Using Deep Learning

Tsietso

Yahya

Samikannu

2022

Mobile Information Systems

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Breast cancer is the most common form of cancer in women. Its aggressive nature has made it one of the chief factors of high female mortality. Therefore, this has motivated research to achieve early diagnosis since it is the best strategy for patient survival. Currently, mammography is the gold standard for detecting breast cancer. However, it is expensive, unsuitable for dense breasts, and an invasive process that exposes the patient to radiation. Infrared thermography is gaining popularity as a screening modality for the early detection of breast cancer. It is a noninvasive and cost-effective modality that allows health practitioners to observe the temperature profile of the breast region for signs of cancerous tumors. Deep learning has emerged as a powerful computational tool for the early detection of breast cancer in radiology. As such, this study presents a review that shows existing work on deep learning-based Computer-aided Diagnosis (CADx) systems for breast cancer detection. In the same context, it reflects on classification utilizing breast thermograms. It first provides an overview of infrared thermography, details on available breast thermogram datasets, and then segmentation techniques applied to these thermograms. We also provide a brief overview of deep neural networks. Finally, it reviews works adopting Deep Neural Networks (DNNs) for breast thermogram classification.

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Geometric transformation-based data augmentation on defect classification of segmented images of semiconductor materials using a ResNet50 convolutional neural network

Cited by 45 publications

References 25 publications

Developing precision agriculture using data augmentation framework for automatic identification of castor insect pests

Developing precision agriculture using data augmentation framework for automatic identification of castor insect pests

A conditional GAN-based approach for enhancing transfer learning performance in few-shot HCR tasks

A Review on Thermal Imaging-Based Breast Cancer Detection Using Deep Learning

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